Network pharmacology-based investigation and experimental validation of the therapeutic potential and molecular mechanism of Danshen Chuanxiongqin injection in acute pancreatitis

2.1 Network pharmacology analysis

The active constituents and their corresponding target data of DCI were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php). Selection of active constituents was based on their oral bioavailability (OB $>$ 20%) and drug-likeness (DL $>$ 0.1). The target genes were standardized to gene symbols using the UniProt database. Prediction of potential targets for Chuanxiong alkaloids was performed using SwissTargetPrediction (http://www.swisstargetprediction.ch), TargetNET (http://targetnet.scbdd.com/), and pharmMapper (http://www.lilab-ecust.cn/pharmmapper/). A comprehensive collection of AP-related genes was retrieved from GeneCards (https://www.genecards.org/), Online Mendelian Inheritance in Man (OMIM, OMIM, https://www.omim.org/), and DisGeNET (https://www.disgenet.org/) databases, and the union of these gene sets was defined as the AP-related gene set. To identify the potential targets of DCI in AP treatment, an intersection analysis was conducted between the target genes of active constituents and the AP-related gene set. Protein-protein interaction data for potential targets were acquired from the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the clusterProfiler R package. Network visualization and analysis were performed using the Cytoscape software, with the Network Analyzer and cytoHubba plugins employed to assess network topology and identify key targets.

2.2 Molecular docking

The protein structure of the designated key target was retrieved from the Protein Data Bank (PDB, https://www.rcsb.org/) database. To prepare the protein for docking analysis, solvent molecules and extraneous protein peptides were eliminated using the PyMOL software. The GetBox plugin was employed to define the docking grid. The structural information of the active compound was obtained from the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database. For docking analysis, the AutoVina molecular docking software was utilized, employing the Lamarckian genetic algorithm for conformational searching. This process generated a total of nine conformations. The final docking conformation was selected based on the highest affinity score. LigPlus (version 2.24) was employed to generate a comprehensive 2D depiction of the protein-ligand complex, while PyMOL was utilized for the three-dimensional visualization of the complex structure.

2.3 Cell culture and treatment

In vitro experiments were performed utilizing the mouse pancreatic acinar cell line 266-6, which was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). The cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (Gibco, Thermo Fisher Scientific), 100 U/mL penicillin, and 100 mg/mL streptomycin. The cells were maintained at 37^∘C in a humidified incubator with 5% CO₂ in a standard atmospheric environment.

The experimental design consisted of four distinct groups: the control group (NC), the model group (CCK), the low-dose Danshen Chuanxiong Injection (DCI) group (CCK + LDCI), and the high-dose DCI group (CCK + HDCI). In the control group, 266-6 cells were solely exposed to 0.1% dimethyl sulfoxide (DMSO) during incubation. The model group was subjected to 5 μ M cholecystokinin (CCK) treatment for 12 hours to induce pancreatic acinar cell injury. The CCK + LDCI and CCK + HDCI groups were treated with varying concentrations of DCI (1 and 10 mL/L) for 30 minutes, followed by exposure to 5 μ M CCK for 12 hours. Subsequently, the cells were harvested for further experimentation.

2.4 CCK-8 cell viability assay

Mouse pancreatic acinar cells (266-6) were cultured in a specific medium supplemented with varying concentrations (0, 1, 5, 10, 20, 30 mL/L) of DCI for a duration of 24 hours. To evaluate cellular toxicity and determine the optimal concentration of DCI for subsequent treatment, the CCK-8 assay was employed. The CCK-8 assay kit was utilized to assess both cell viability and the potential toxic effects of DCI. In brief, cells from each experimental group were seeded at a density of 2 × 104 cells per well in a 96-well plate. Following an appropriate incubation period, 10 μ L of CCK-8 solution was added to each well, followed by an additional 2-hour incubation. The enzymatic reaction was subsequently terminated, and the absorbance at 450 nm was measured using a microplate reader.

2.5 Western blot

Immunoblot analysis was performed to evaluate the protein content in pancreatic tissues. The BCA Protein Assay Kit (Thermo Fisher Scientific, MA, USA) was utilized for protein quantification. Subsequently, protein samples were resolved on 10% SDS-PAGE gels and transferred onto PVDF membranes. To block non-specific binding, the membranes were incubated with 5% skim milk at room temperature for 2 hours. Following this, the membranes were subjected to overnight incubation at 4^∘C with primary antibodies against AKT1 (diluted 1:100), NFKB p65 (diluted 1:100), P53 (diluted 1:100), and GAPDH (diluted 1:250) in a suitable blocking buffer. Subsequently, the membranes were thoroughly washed with TBST (three washes, 10 minutes each) and incubated with the corresponding horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 hour. After a final wash, protein bands were visualized using the ECL Plus chemiluminescence system.

2.6 Quantitative real-time PCR

Acinar cells were isolated and cultured in 6-well plates. Subsequent to treatment (or harvesting pancreatic tissues from mice), total RNA was extracted from the cells/tissues using TRI reagent (Molecular Research Center, Cincinnati, OH, USA), and RNA integrity was evaluated by assessing the A260/280 ratio. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed following established protocols [18]. The relative expression levels of the target genes were normalized to the reference gene GAPDH. The primer sequences employed for RT-qPCR analysis are documented in Table 1.

Statistical analysis was conducted using GraphPad Prism 9 software (GraphPad, San Diego, CA, USA). The comparison between two groups was assessed using the t -test. The data were represented as the mean ± SEM (standard error of the mean), with a significance level of 0.05 denoting statistical significance (two-tailed).

3.1 Active ingredients and targets of DCI

Through the utilization of the TCMSP database, a comprehensive analysis revealed that DANSHEN encompassed a repertoire of 143 bioactive constituents, which exhibited associations with 309 molecular targets. Moreover, ligustrazine was predicted to have potential interactions with 137 target molecules. To visually represent this intricate network, Fig. 1 presents a diagram illustrating the interplay between these 144 active ingredient nodes and their corresponding 430 targets.

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The discernment of DCI’s putative therapeutic targets in AP was accomplished by intersecting its molecular targets with a set of AP-related genes, as depicted in Fig. 2A. A comprehensive screening yielded a total of 93 potential targets, which were found to exhibit extensive interactions as demonstrated in Fig. 2B. In total, 1594 interaction information was extracted from the STRING database. A network analysis further revealed that this network possessed an average degree value of 35. Notably, AKT serine/threonine kinase 1 (AKT1) and albumin (ALB) emerged as pivotal proteins in the interactome, both exhibiting the highest degree centrality with 75, signifying their integral roles in the network. The KEGG enrichment analysis conducted on these targets unveiled their association with 231 pathways, including noteworthy pathways such as the TNF signaling pathway, IL-17 signaling pathway, and HIF-1 signaling pathway, as visualized in Fig. 2C. Additionally, our GO enrichment analysis revealed significant engagement of the identified targets in critical biological processes linked to AP progression, notably oxidative stress response, cytokine production regulation, and leukocyte trafficking, as depicted in Fig. 2D.

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According to the p -values of enrichment analysis, the top 10 signaling pathways were selected and a pathway-target network was constructed (Fig. 3A). The enrichment analysis revealed that the PI3K-AKT and MAPK signaling pathways exhibited a significant enrichment of targets. These two signaling pathways have been extensively studied in relation to AP pathology and serve as targets for numerous drugs targeting AP [19, 20]. Moreover, a pathway-target-active ingredient network was established in order to identify key targets and active ingredients (Fig. 3B). Notably, quercetin (MOL000098, degree = 34), prostaglandin-endoperoxide synthase 2 (PTGS2, degree = 151), and the PI3K-AKT signaling pathway (degree = 22) emerged as the most crucial active ingredient, target, and signaling pathway, respectively, based on their respective degree values. Finally, employing the cytoHubba analysis based on degree values, 10 hub targets were identified (Fig. 3C).

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By employing molecular docking techniques, we conducted a thorough evaluation of the binding affinities between AKT1 and NFKB1A with the respective active compounds, as presented in Table 2. Exceptionally, the docking complex composed of AKT1 and beta-carotene displayed a binding energy surpassing - 5 kcal/mol, whereas all other complexes manifested lower binding energies ( - 5 kcal/mol or less), indicating stronger interactions. Notably, the docking complex formed by tanshinone iia and NFKB1A exhibited the lowest binding energy, indicating a potential strong binding interaction. Furthermore, the binding energies between NFKB1A and the active compounds were consistently lower compared to those between AKT1 and the same active compounds. Additionally, our analysis revealed the presence of extensive hydrogen bonding and van der Waals forces within the binding complexes of AKT1/NFKB1A and the active compounds.

3.5 DCI regulates the expression of key target genes and proteins to alleviate acinar cell injury

To further elucidate the protective effect of DCI, we established a cell damage model using the 266-6 cell line induced by CCK. Initially, cell viability experiments were conducted to assess the cytotoxicity of DCI at concentrations ranging from 0 to 10 ml/L on acinar cells (Fig. 4A). Notably, no discernible cytotoxic effects were detected, thereby substantiating the safety profile of DCI when applied to pancreatic acinar cells. Subsequently, we proceeded to investigate the protective effect of DCI on the CCK-induced damage model by pretreating acinar cells with varying doses of DCI. The results demonstrated that both

low-dose DCI (LDCI) and high-dose DCI (HDCI) significantly ameliorated the decreased cell viability caused by CCK (Fig. 4B). Additionally, western blot analysis was performed to assess the protein expression levels of AKT1, NFKB p65, and P53. Remarkably, CCK stimulation led to a significant increase in the protein expression of these factors, whereas DCI intervention exerted a dose-dependent reduction in their expression levels (Fig. 4C). Furthermore, to validate the impact of DCI on other key target genes, quantitative polymerase chain reaction (qPCR) was conducted. The results demonstrated that, apart from VEGFA and RXRA, CCK stimulation significantly upregulated the expression of other genes, whereas DCI intervention significantly attenuated the upregulation of gene expression induced by CCK (Fig. 4D).

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